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Special Session on Conformal Prediction and its Applications (CoPA 2015 Workshop) at the 3rd International Symposium on Learning and Data Sciences (SLDS 2015) Royal Holloway, University of London, UK, April 20 - 22, 2015 http://www.clrc.rhul.ac.uk/slds2015/ ======================================================================================= Important Dates: ================ Full paper submission: November 21st, 2014 Author Notifications: December 19th, 2014 Camera-ready submission: January 16th, 2015 Early Registration: January 16th, 2015 Theme: ====== Quantifying the uncertainty of the predictions produced by classification and regression techniques is an important problem in the field of Machine Learning. Conformal Prediction is a recently developed framework for complementing the predictions of Machine Learning algorithms with reliable measures of confidence. The methods developed based on this framework produce well-calibrated confidence measures for individual examples without assuming anything more than that the data are generated independently by the same probability distribution (i.i.d.). Since its development the framework has been combined with many popular techniques, such as Support Vector Machines, k-Nearest Neighbours, Neural Networks, Ridge Regression etc., and has been successfully applied to many challenging real world problems, such as the early detection of ovarian cancer, the classification of leukaemia subtypes, the diagnosis of acute abdominal pain, the assessment of stroke risk, the recognition of hypoxia in electroencephalograms (EEGs), the prediction of plant promoters, the prediction of network traffic demand, the estimation of effort for software projects and the backcalculation of non-linear pavement layer moduli. The framework has also been extended to additional problem settings such as semi-supervised learning, anomaly detection, feature selection, outlier detection, change detection in streams and active learning. The aim of this special session is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between researchers on any aspect of Conformal Prediction and its applications. The special session welcomes submissions introducing further developments and extensions of the Conformal Prediction framework and describing its application to interesting problems of any field. Topics of Interest ================== Topics of interest include, but are not limited to: * Non-conformity measures * Modifications of the framework * Venn prediction * On-line compression modeling * Extensions to additional problem settings * Theoretical analysis of Conformal Prediction techniques * Applications/usages of Conformal Prediction Submission ========== Authors are invited to submit original, English-language research contributions or experience reports. Papers should be no longer than 10 pages formatted according to the well-known LNCS/LNAI Springer style. All aspects of the submission and notification process will be handled online via the EasyChair Conference System at: http://www.easychair.org/conferences/?conf=slds2015 Please make sure you select the "Special Session: Conformal Prediction and it's Applications" track in the first step of the submission process. Publication =========== Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. Accepted papers will be presented at the Symposium and published by Springer in a volume of the Lecture Notes in Artificial Intelligence (LNAI) series. They will also be considered for potential publication in the Special Issues of the Symposium. Honorary Chairs =============== Vladimir Vapnik NEC, USA & Royal Holloway, University of London, UK Alexei Chervonenkis Russian Academy of Sciences, Russia & Royal Holloway, University of London, UK Program Chairs ============== Harris Papadopoulos Frederick University, Cyprus Email: h.papadopou...@frederick.ac.cy Alex Gammerman Royal Holloway, University of London, UK Email: a...@cs.rhul.ac.uk Vladimir Vovk Royal Holloway, University of London, UK Email: v...@cs.rhul.ac.uk |
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